
Random Over-Sampling (ROS) [1]} without and with data augmentation (ROS and ROS+). For the augmentations we used: random sized cropping between 0.15 and 1.1 scale of the image, random horizontal flip, and random color/brightness/contrast jitter. A visualization of ROS and ROS+ is presented in Figure REF . During meta-training, ROS+ augmentations were applied twice: once when sampling from the meta-training dataset, and the second time during the support set resampling. This may have slightly destabilized meta-training, which would explain why sometimes Random-Shot with ROS (ROS+ at inference only) achieved better performance than Random-Shot with ROS+ in Figures REF  and REF .

Random-Shot Meta-Training [2]}, [3]}, [4]} was applied as specified in the main body of the paper (Section REF ).

Focal Loss [5]}. Focal Loss has been found to be very effective in combating the class-imbalance problem on the one-stage object detectors. We exchanged the inner-loop cross-entropy loss of optimization-based algorithms and fine-tune baselines with the focal loss with \(\gamma =2\)  and \(\alpha =1\) . Results are presented in Figure REF  in the main paper body and in Appendix REF .

Weighted Loss. Weighted loss is also commonly used to rebalance the effects of class-imbalance [6]}, [7]}. We weight the inner-loop cross-entropy loss of optimization-based algorithms and fine-tune baselines by inverse class frequency of support set samples. Results are presented in Figure REF  in the main paper body and in Appendix REF .

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